A serious complication of pregnancy, preeclampsia, is a major source of maternal and fetal morbidity and mortality. Preeclampsia is a heterogeneous condition variably involving different organ systems. Despite decades of investigation there are no known biomarkers that can predict the different preeclampsia phenotypes and no treatment other than delivering the placenta and baby. There is an urgent need to apply an alternative methodology that would allow identification of predictive biomarkers for different PE phenotypes and corresponding therapeutic targets. Dynamical modeling uses differential equations to describe the behavior of a system and in contrast to statistical modeling allows computation of the outcomes of experiments that are significantly different from the ones used to build and calibrate the model. The model's initial conditions and parameters are determined by patient's phenotype and genetic makeup. By varying them it is possible to simulate a wide variety of patients. A patient's development can be analyzed through numerical solution of the model equations. The impact of up- or down-regulating a potential therapeutic target, various treatment protocols, and combined treatment aimed at multiple therapeutic targets can be simulated directly through corresponding modifications of the equations and parameters. Therefore, dynamical modeling is a perfect tool for development of personalized treatments. It has been successfully applied to research various types of cancer, diabetes, arthritis, stroke, cardiovascular, metabolic, hematologic, autoimmune, neurodegenerative and ophthalmological diseases. However, with one exception, pregnancy complications have not yet been studied via dynamical modeling. We propose to stimulate ongoing research with a dynamical model of the development of preeclampsia. Our long term goals are: 1) to encode the complex interrelationships among various mediators of the preeclampsia pathophysiology in a dynamical model and calibrate it using existing quantitative data; 2) to utilize the model to derive early composite biomarkers for various preeclampsia phenotypes; and 3) to simulate, in silico, large-scale, randomized clinical trials to identify potential therapeutic targets and personalized treatment protocols for women with preeclampsia. The overall objective of this proposal, which is the first step towards the long-term goals, is to formulate a robust dynamical model of preeclampsia pathophysiology in terms of measurable variables and calibrate it with the existing data. We will first create and validate a rich database of existing cross-sectional and longitudinal datasets from prior studies of women with preeclampsia. We will then develop and validate a robust dynamical model explaining the maternal and placental components, the primary processes and dynamics of early onset preeclampsia displaying the range of clinical behaviors and accounting for fetal sex. We will then extend the model to incorporate late onset preeclampsia and potentially other subtypes.